Covid Report for Family

Background

Notes and format last updated May 7, 2020

Starting on the May 7th update, the NY Times began including probable covid cases/deaths along with confirmed. This mostly affects death counts – for certain geographies that include probable COVID deaths in addition to confirmed, these are now added to the totals. For the time being, they were all added to the May 6th totals, causing a big spike at the U.S. level. Over time, NY Times will revise their historical counts and distribute these added deaths when they actually occurred, so the spike should fade.

• This page shows COVID data broken down into regions that affect our family. It pulls from publicly available raw data from the NY Times, which is reported daily. It’s on a one-day lag, so when you see this report, the most recent day included will be that reported the day prior.
• The table of contents above summarizes the major sections and can be used to jump to those sections.
• Each major section is then divided into tabs to allow you to more easily shuffle between geographies and metrics.
  • If you open on your phone, I don’t believe the tabs will work, so you’ll just see the page as one long, continuous document.
• More detailed descriptions and context can be found in each section.

Growth rates

Heat maps

• The two heat maps below compare how quickly total cases or deaths have grown at various times in our respective geopgraphies.
• The first plot compares growth rate for total cases; the second, growth rate for total deaths.
• The metric used is doubling time, by which I mean how quickly total cases or deaths are doubling.
• The plots track that doubling time at each date for our geographies. Darker colors reflect shorter doubling times, and thus periods of faster growth.
  • You can use the plots to track each geography over time and to compare the geographies to one another.
  • You can also compare the cases and death charts, to see how faster periods of death growth follow faster periods of case growth.

Case growth rates

• This section charts the growth rate of both total and new cases for each of our respective geographies. Each geography has its own chart, and then that chart will have a trendline for total cases and new cases.
  • There are only plots for the U.S. and states because the numbers for the counties are too small to generate worthwhile trendlines in this section.
• Note that we’re charting growth rate and not a count of cases, so don’t think of these as the standard “curve” that we hear about in the news and that we want to flatten. Instead, these growth rate charts help track more precisely what we can only estimate when we see those other curves. For these growth rate charts, if the line is above zero, the metric we are tracking (total or new cases) is continuing to grow. If the growth rate line is going up, it’s growing more quickly each day; if it’s going down but still above zero, it’s growing less quickly (but still growing). Only when the growth rate lines go below zero has the metric stopped growing.
• Each of these two lines uses rolling windows to calculate a growth rate for that particular metric. I do the calculation differently for each to smooth out some of the large day-to-day discrepancies in new case reporting at the state level.
  • For total cases, the trendlines are a rolling 3-day average of daily growth rates in total cases. We want to see these decline (and almost all are), but they can’t go below zero. This is because we’re tracking growth rate and a growth rate line below zero would mean total cases have gone down, which can’t happen. They can only grow less quickly, which means we want to see the total case line get as close to zero as possible.
  • For new cases, the trendlines show a rolling 3-day average of daily growth rate in the rolling 7-day average of new cases. Including two rolling periods in this average helps smooth out crazy spikes at the state level that result from large day-to-day changes. Unlike the lines for total cases, we want to watch for the lines for new cases to get consistently below zero and stay there. That means that we are consistently seeing fewer new cases on a daily basis.

U.S.

Our states

Death growth rates

• This section charts the growth rate of both total and new deaths for each of our respective geographies. Each geography has its own chart, and then that chart will have a trendline for total deaths and new deaths.
  • There are only plots for the U.S. and states because the numbers for the counties are too small to generate worthwhile trendlines in this section.
• Note that we’re charting growth rate and not a count of deaths, so don’t think of these as the standard “curve” that we hear about in the news and that we want to flatten. Instead, these growth rate charts help track more precisely what we can only estimate when we see those other curves. For these growth rate charts, if the line is above zero, the metric we are tracking (total or new deaths) is continuing to grow. If the growth rate line is going up, it’s growing more quickly each day; if it’s going down but still above zero, it’s growing less quickly (but still growing). Only when the growth rate lines go below zero has the metric stopped growing.
• Each of these two lines uses rolling windows to calculate a growth rate for that particular metric. I do the calculation differently for each to smooth out some of the large day-to-day discrepancies in new death reporting at the state level.
  • For total deaths, the trendlines are a rolling 3-day average of daily growth rates in total deaths. We want to see these decline (and almost all are), but they can’t go below zero. This is because we’re tracking growth rate and a growth rate line below zero would mean total deaths have gone down, which can’t happen. They can only grow less quickly, which means we want to see the total death line get as close to zero as possible.
  • For new deaths, the trendlines show a rolling 3-day average of daily growth rate in the rolling 7-day average of new deaths. Including two rolling periods in this average helps smooth out crazy spikes at the state level that result from large day-to-day changes. Unlike the lines for total deaths, we want to watch for the lines for new deaths to get consistently below zero and stay there. That means that we are consistently seeing fewer new deaths on a daily basis.

U.S.

Our states

By population rankings

This section tracks metrics for states and counties normalized for population (number of cases or deaths per million residents), and then compares these figures both for our geographies and the country overall.

States

• This section shows tables ranking all 50 states for per populations rates of total cases, new cases, total deaths, and new deaths.
• For each metric, in addition to the tables, the trends for the top states are plotted over time.
  • We only plot the top ten states for each metric so that the plots aren’t too crowded. But you can view the full 50-state rankings in the tables.

Total confirmed cases

Table of total confirmed cases per million residents (all 50 states)

Ranking	State	Cases Per Million
1	North Dakota	115,314
2	South Dakota	102,907
3	Iowa	81,218
4	Wisconsin	80,542
5	Nebraska	77,048
6	Utah	72,959
7	Montana	68,585
8	Wyoming	68,007
9	Idaho	67,844
10	Minnesota	67,182
11	Illinois	67,110
12	Rhode Island	66,849
13	Tennessee	64,825
14	Kansas	64,757
15	Indiana	63,615
16	Arkansas	61,535
17	Nevada	60,874
18	Mississippi	60,295
19	Alabama	60,293
20	Oklahoma	60,063
21	Missouri	59,185
22	Louisiana	57,781
23	New Mexico	57,133
24	Arizona	56,142
25	Alaska	55,955
26	Florida	52,422
27	Texas	51,043
28	Kentucky	50,720
29	Colorado	50,302
30	Georgia	49,313
31	South Carolina	48,984
32	Ohio	48,141
33	Delaware	46,684
34	Michigan	46,521
35	New Jersey	45,317
36	Massachusetts	42,158
37	North Carolina	41,633
38	Connecticut	41,163
39	California	40,257
40	New York	40,138
41	Maryland	38,866
42	Pennsylvania	38,736
43	West Virginia	35,274
44	District of Columbia	35,244
45	Virginia	33,027
46	Puerto Rico	30,073
47	Washington	27,496
48	New Hampshire	22,766
49	Oregon	22,251
50	Hawaii	13,632
51	Maine	11,845
52	Vermont	9,219

New confirmed cases

Table of new cases per million residents: rolling 3-day average (all 50 states)

Ranking	State	New Cases Per Million
1	Tennessee	1,161
2	Oklahoma	1,028
3	Indiana	1,016
4	Ohio	880
5	Texas	877
6	Nevada	875
7	South Dakota	870
8	Pennsylvania	852
9	Arizona	851
10	California	840
11	Delaware	829
12	Utah	827
13	Alaska	814
14	New Mexico	805
15	Massachusetts	763
16	Arkansas	756
17	Montana	732
18	Alabama	728
19	Mississippi	727
20	Kentucky	708
21	Wisconsin	690
22	Minnesota	685
23	South Carolina	681
24	Illinois	664
25	New Hampshire	657
26	West Virginia	655
27	Wyoming	654
28	Colorado	622
29	North Carolina	594
30	Kansas	561
31	Nebraska	555
32	Missouri	552
33	Iowa	551
34	New York	541
35	Idaho	539
36	New Jersey	527
37	Georgia	526
38	Louisiana	522
39	North Dakota	509
40	Rhode Island	494
41	Maryland	485
42	Florida	484
43	Virginia	424
44	District of Columbia	366
45	Washington	359
46	Connecticut	353
47	Michigan	347
48	Oregon	317
49	Puerto Rico	310
50	Maine	263
51	Vermont	181
52	Hawaii	94

Total deaths

Table of total deaths per million residents (all 50 states)

Ranking	State	Deaths Per Million
1	New Jersey	1,998
2	New York	1,809
3	Massachusetts	1,646
4	North Dakota	1,519
5	Connecticut	1,504
6	Louisiana	1,466
7	Rhode Island	1,424
8	South Dakota	1,423
9	Mississippi	1,410
10	Illinois	1,210
11	Michigan	1,120
12	Iowa	1,017
13	District of Columbia	1,013
14	Arizona	1,010
15	Indiana	1,010
16	Pennsylvania	985
17	Arkansas	975
18	New Mexico	933
19	Florida	924
20	Georgia	921
21	South Carolina	920
22	Maryland	846
23	Texas	844
24	Delaware	837
25	Alabama	836
26	Nevada	826
27	Minnesota	798
28	Tennessee	791
29	Missouri	775
30	Montana	765
31	Wisconsin	746
32	Nebraska	718
33	Kansas	711
34	Colorado	691
35	Idaho	659
36	Ohio	640
37	North Carolina	558
38	Wyoming	554
39	Kentucky	549
40	West Virginia	540
41	California	532
42	Oklahoma	521
43	Virginia	516
44	New Hampshire	443
45	Washington	399
46	Puerto Rico	398
47	Utah	329
48	Oregon	274
49	Alaska	231
50	Hawaii	192
51	Maine	191
52	Vermont	152

New deaths

Table of new deaths per million residents: rolling 3-day average (all 50 states)

Ranking	State	New Deaths Per Million
1	South Dakota	30
2	North Dakota	21
3	New Mexico	17
4	Kansas	14
5	Minnesota	14
6	Pennsylvania	14
7	Arkansas	13
8	Illinois	12
9	Mississippi	12
10	Wyoming	12
11	Colorado	10
12	Indiana	10
13	Nevada	10
14	Tennessee	10
15	Alaska	9
16	Iowa	9
17	Michigan	9
18	Montana	9
19	Arizona	8
20	West Virginia	8
21	Nebraska	7
22	Oklahoma	7
23	South Carolina	7
24	Wisconsin	7
25	Idaho	6
26	Louisiana	6
27	Massachusetts	6
28	Texas	6
29	Maryland	5
30	New Jersey	5
31	New York	5
32	Ohio	5
33	Alabama	4
34	Florida	4
35	Kentucky	4
36	New Hampshire	4
37	Utah	4
38	California	3
39	Connecticut	3
40	Delaware	3
41	District of Columbia	3
42	Georgia	3
43	Missouri	3
44	North Carolina	3
45	Puerto Rico	3
46	Rhode Island	3
47	Vermont	3
48	Maine	2
49	Oregon	2
50	Virginia	2
51	Hawaii	1
52	Washington	-1

Counties

• This section focuses on the county level. It shows tables with our counties ranked by percentile of U.S. counties for per population rates of total cases and total deaths.
  • Each table also shows the top five counties in the country in addition to our counties, for added perspecive.
• In addition to the tables, our counties’ percentile for both total cases and total deaths are plotted over time.

Confirmed cases

Table showing total cases per million and percentile for all US counties. Includes our counties and the top 5 in the US for perspective.

County	State	Cases Per Million	Raw Ranking	Percentile
Crowley	Colorado	235,935	1	99
Norton	Kansas	211,155	2	99
Bon Homme	South Dakota	206,347	3	99
Lincoln	Arkansas	204,622	4	99
Buffalo	South Dakota	204,383	5	99
Davidson	Tennessee	78,387	503	83
Richland	South Carolina	55,017	1432	54
York	South Carolina	43,053	2101	33
Orange	California	33,006	2538	19
Pierce	Washington	25,635	2771	11

Our county percentiles over time

Deaths

Table showing total deaths per million and percentile for all US counties. Includes our counties and the top 5 in the US for perspective.

County	State	Deaths Per Million	Raw Ranking	Percentile
Gove	Kansas	7,587	1	99
Jerauld	South Dakota	7,452	2	99
Dickey	North Dakota	6,568	3	99
Foster	North Dakota	5,919	4	99
Gregory	South Dakota	5,735	5	99
Richland	South Carolina	755	1575	49
Davidson	Tennessee	684	1723	45
York	South Carolina	555	1981	36
Orange	California	533	2031	35
Pierce	Washington	323	2502	20

Our county percentiles over time

Raw counts

• This section shows the raw counts over time for the U.S., our states, and our counties for four categories:
  • Total confirmed cases: This is the cumulative total of cases reported.
  • New confirmed cases: This is the number of new cases reported on each respective date.
    • For the U.S. and states, a trendline shows the rolling 7-day average of new cases, to help better visualize the peak.
  • Total deaths: This is the cumulative total of deaths reported.
  • New deaths: This is the number of new deaths reported on each respective date.
    • For the U.S. and states, a trendline shows the rolling 7-day average of new deaths, to help better visualize the peak.

Total confirmed cases

U.S.

Our states

Our counties

New confirmed cases

U.S.

Our states

Our counties

Total deaths

U.S.

Our states

Our counties

New deaths

U.S.

Our states

Our counties

Stay-at-home comparisons

• This section is a work in progress. It’s exploring what effect stay-at-home orders have on spread. It’s inherentely messy because some states have localized orders but not orders for their full state and because states implemented the orders at different times.
  • The first plot shows a rolling 3-day average of new cases per million residents, averaged across 2 groups of states – those with a state-wide stay-at-home order in effect and those without. The size of the dots reflect the number of states in each group for each given day (so the dots are getting bigger over time for the group with orders, as more states implement those orders).
    • So far, this view doesn’t show if these orders are “working” or not. Instead, the biggest takeaway from the current plot is that the states that implemented orders are reacting to having worse spread, which is why the case rate is so much higher at the start for them. As a state decides it has a problem and implements an order, it joins the grouping of states with orders and drives the rate up for that group.
    • Over time (if the policies work), what we’d expect is that the line for the group with orders flattens or declines in relation to the no-order group. It also may become moot if all states issue orders.
    • As states start rescending their orders, I’ll put them back into the no-order group, so the no-order group should beging to expand again.
  • The second plot tracks progress of states by the number of days since orders have been in effect. It includes only those states with orders and shows their average new case growth rate (a rolling 3-day average of daily growth rates) based on how long it’s been since the order went into effect. Again, the size of the dots show how many states are included in each point – i.e., as you go further right, the dots are smaller because less states are that far past their order.
    • I’m working on how to add a baseline to this chart to compare to states without orders, but that’s challenging because there’s no way to fully align timing.